Fuzzy Information Fusion Research Articles

Using Big Data Fuzzy K‐Means Clustering and Information Fusion Algorithm in English Teaching Ability Evaluation

Aiming at the problem of inaccurate classification of big data information in traditional English teaching ability evaluation algorithms, an English teaching ability evaluation algorithm based on big data fuzzy K‐means clustering and information fusion is proposed. Firstly, the author uses the idea of K‐means clustering to analyze the collected original error data, such as teacher level, teaching facility investment, and policy relevance level, removes the data that the algorithm considers unreliable, uses the remaining valid data to calculate the weighting factor of the modified fuzzy logic algorithm, and evaluates the weighted average with the node measurement data and gets the final fusion value. Secondly, the author integrates the big data information fusion and K‐means clustering algorithm, realizes the clustering and integration of the index parameters of English teaching ability, compiles the corresponding English teaching resource allocation plan, and realizes the evaluation of English teaching ability. Finally, the results show that using this method to evaluate English teaching ability has better information fusion analysis ability, which improves the accuracy of teaching ability evaluation and the efficiency of teaching resources application.

Design of Geographic Information Visualization System for Marine Tourism Based on Data Mining

Zhu, W.; Hou, Y.; Wang, E., and Wang, Y., 2020. Design of geographic information visualization system for marine tourism based on data mining. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 1034–1037. Coconut Creek (Florida), ISSN 0749-0208.This paper presents a design scheme of marine tourism geographic information visualization system based on big data fusion and B/S architecture. The software development design of marine tourism geographic information visualization system is carried out under embedded Linux kernel and B/S architecture. The algorithm design of sea tourism geographic information visualization is carried out by using big data mining and fuzzy information fusion clustering method. The software design consists of marine tourism geographic information collection module, marine tourism geographic information BIM reconstruction module, database module, marine tourism geographic information interaction module and integrated information processing module, etc. The basic database structure of marine tourism geographic information visualization system is constructed under the B/S structure system, and program loading and automatic compilation design are adopted to realize the software development of marine tourism geographic information visualization system under bus control and embedded environment. The test results show that the information integration and processing ability of marine tourism geographic information visualization is strong, the visualization information fusion and mining performance of marine tourism are good, and the result of visual analysis of marine tourism geographic information is stable and reliable.

Generalized hesitant fuzzy information fusion using extended partitioned Bonferroni mean operator with application in decision-making

The partitioned Bonferroni mean operator (PBM), which was formulated to outspread the family of the Bonferroni mean operator, has augmented the class of aggregation functions for information accumulation by modeling interrelationship among pairwise disjoint partition sets. It is constructed with the presupposition that the criteria set is subdivided into mutually disjoint partition sets, with homogeneous interconnection among input arguments of intra-partition sets. The PBM operator has accomplished a lot of desirability from the researchers due to its capability of apprehending interconnection among arguments in the information accumulation technique. The central idea of this study is to amplify the existing PBM definition systematically so that heterogeneous interconnections among the criteria of intra-partition sets can be captured. This contemplation prompted us to focus on the systematized proposition of extended partitioned Bonferroni mean (EPBM) operator based on heterogeneous connections of the information retrieved from the partition sets. In this aspect, we also propose the hesitant fuzzy extended partitioned Bonferroni mean (HFEPBM) operator, along with its weighted generalization (WHFEPBM operator), by fitting the concept of strict t-norms and t-conorms into it. To intensify the capacity for modeling real-life decision situations, the extended TOPSIS method and the proposed operator have been employed to detect the weights of decision-makers. A numerical example has been presented to demonstrate the experimental results obtained by utilizing the WHFEPBM operator. The contribution ends by providing a detailed comparative analysis of the proposed method with other existing methods by availing data through the simulation experiment.

Developing the modified R-numbers for risk-based fuzzy information fusion and its application to failure modes, effects, and system resilience analysis (FMESRA)

In order to identify and eliminate known or potential failures from the process of product design, development and production, failure mode and effect analysis (FMEA) have been widely used in a variety of industries as a useful tool in prognostics and health management, safety and reliability analysis. The traditional FMEA shows two significant flaws while calculating the risk priority number (RPN). First, recovery time that considerably affects the safety, cost, and sustainability of the system is not considered in the RPN calculation. Second, in order to capture different conflicting experts’ views, especially when the obtained data are fuzzy, there is no mechanism. In order to overcome these issues, this paper presents a resilience-based risk priority number for considering the recovery and repair time of each failure mode, then a risk-based fuzzy information processing and decision-making is developed by modifying the R-numbers methodology and on the basis of simultaneous evaluation of criteria and alternatives (SECA) approach which is so-called R-SECA method. The capability of proposed models is tested by a case study of a centrifugal air compressor in a steel manufacturing company. Results show the robustness of proposed R-SECA model in dealing with different scenarios of risky information.

Application of smart safety training and education in network teaching management

Aiming at the problems of poor resource scheduling and low degree of information fusion in the traditional network management method of intelligent security training and education optimization, an intelligent security training and education optimization network management model based on big data mining is proposed. Building intelligent safety training and education of big data fusion analysis model, using the method of association rules mining, complete the intelligent safety training and education statistics analysis, under the Internet environment using quantitative sensing fusion tracking method, the network teaching management information fusion processing, build large data information scheduling model based on network teaching management, fuzzy information fusion method to reconstruct 3 d information of the network teaching management, to establish the network teaching management big data spectral analysis model, the introduction of phase space reconstruction method, the network resource scheduling optimization of teaching management. The experimental results show that the proposed method has better resource scheduling performance, higher degree of information fusion, and can improve the ability of intelligent security training and education management.

Fuzzy adaptive nonlinear information fusion model predictive attitude control of unmanned rotorcrafts

This study proposes a novel nonlinear information fusion model predictive control (NIFMPC) approach for the attitude control of unmanned rotorcraft subject to external wind disturbance. In the proposed control structure, the fusion estimation of control input is obtained by fusing the soft constraint information and the hard one via the nonlinear information fusion estimation method. The output error convergence of the closed-loop system is analyzed using the Lyapunov stability theory. In order to avoid input saturation, the fuzzy logic system (FLS) is adopted to design the adaptive fuzzy gain-scheduling (AFGS)-NIFMPC, in which the soft constraint information related to control action are adaptively scheduled according to fuzzy rules, with control input and its rate of change as FLS inputs and soft constraint information as FLS outputs. Finally, simulation results are presented to demonstrate the effectiveness and robustness of the proposed control strategies.

Recommendation of PHM algorithms based on fuzzy information fusion

The diversity of algorithms and the complication of application scenarios makes it difficult for PHM (Prognostics and Health Management) developers to accurately choose an appropriate algorithm when performing algorithm design. To make up for the aforementioned shortcoming, this paper proposes an algorithm recommendation system suitable for PHM. Specifically, according to the PHM architecture, a PHM database is designed to assist in the mining of recommended knowledge and the execution of recommended strategies. Subsequently, a recommendation system is developed that mainly includes hybrid data processing, similarity measurement, and recommendation decision making. Therein, two recommendation strategies, based on Fuzzy C-Means (FCM) method and weighted information fusion, are designed to cope with two kinds of actual requirements. Finally, the recommendation of remaining useful life (RUL) prediction associated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set is employed as an application case to verify the effectiveness of the proposed recommendation system.

Application of multi-sensor fuzzy information fusion algorithm in industrial safety monitoring system

Chemical enterprises are characterized by many casualties and frequent serious accidents. In order to reduce the likelihood of such accidents and ensure safe production effectively, a new safety assessment method based on multi-sensor technology and fuzzy information fusion algorithm is proposed based on theory of fuzzy systems. The important parameters such as dust concentration, temperature and smoke concentration are monitored by various sensors in chemical enterprises, and information obtained by multiple sensors are fuzzified and optimized through the compositional operation and decision rules of a data fusion center, so as to fully utilize the information obtained from each monitoring point and obtain accurate estimations of the environmental safety state. In this paper, an example is presented to illustrate that the multi-sensor fuzzy information fusion algorithm increases the confidence level of industrial safety monitoring and improves the performance of the safety monitoring system.

Construction of Integrated Management Model of Port Logistics Supply Chain Based on Big Data

Ouyang, F., 2019. Construction of integrated management model of port logistics supply chain based on big data. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1053–1058. Coconut Creek (Florida), ISSN 0749-0208.Port logistics supply chain management is an integrated management idea and method, which performs the functions of logistics planning and control from supplier to end user in supply chain, an integrated management model of port logistics supply chain based on big data is proposed. The big data regression analysis model of integrated management of port logistics supply chain is constructed by statistical analysis method. The constraint index parameter set of optimization of integrated management mode of port logistics supply chain is analyzed in port logistics supply chain, and the big data fusion of integrated management model of port logistics supply chain is carried out by using correlation detection method. Combined with cumulative variance analysis, big data clustering is carried out for port logistics supply chain integrated management model. According to the clustering results, the quantitative distribution set of port logistics supply chain integrated management model is constructed. Combined with big data fuzzy clustering control and information fusion method, the port logistics supply chain integrated management model is constructed. The results of empirical analysis show that the data fusion of port logistics supply chain integrated management model selection by this method is good, which shows that the rationality of port logistics supply chain integrated management mode selection is more accurate, and the integrated management ability of port logistics supply chain under the environment of Internet of things is improved.

Multiple target information fusion matching algorithm based on a line laser and a single plane array camera.

In the testing process of the dynamic multitarget parameter of a weapon range, multiple targets often occur in the same plane simultaneously, so that the existing optical testing methods cannot recognize and match the spatial positions of all the targets. To address this problem, we propose a new optical testing method that combines a line laser with a plane array camera, establishing a multiparameter calculation model using the intersection of the imaging optical axis of a plane array camera and the two line-laser detection screens. Based on the established multiparameter model, the recognition model based on time-space information constraint relationship between the line laser and plane array camera is developed, and it provides a constraint condition discriminant function. We also introduce the recognition and matching method based on fuzzy information fusion, analyze the matching mechanism of correlation parameters of the data fusion algorithm, and provide the solution method of key parameters. On the basis of the inherent parameters of the proposed testing method, the multitarget images at different velocities are collected, and the test data under the contrast experimental conditions is obtained. The results show that the proposed testing method can effectively solve the matching problem when multitargets appear at the same time causing occlusion and overlapping. The test error meets the measurement requirements.

A 2-tuple integrated DEA-based approach for neuromarketing technology evaluation

Purpose Neuromarketing, which is an interdisciplinary area, concentrates on evaluating consumers’ cognitive and emotional reactions to different marketing stimuli. In spite of advantages, neuromarketing still requires development and lacks a strong theoretical framework. Techniques that are used in neuromarketing studies have different superiorities and limitations, and thus, there is a need for the evaluation of the relevance of these techniques. The purpose of this study is to introduce a novel integrated approach for the neuromarketing research area. Design/methodology/approach The proposed approach combines 2-tuple linguistic representation model and data envelopment analysis to obtain the most efficient neuromarketing technique. It is apt to handle information provided by using both linguistic and numerical scales with multiple information sources. Furthermore, it allows managers to deal with heterogeneous information, without loss of information. Findings The proposed approach indicates that functional magnetic resonance imaging (fMRI) is the best performing neuromarketing technology. Recently, fMRI has been widely used in neuromarketing research. In spite of its high cost, its main superiorities are improved spatial and temporal resolutions. On the other hand, transcranial magnetic stimulation (TMS) and positron emission tomography (PET) are ranked at the bottom because of their poor resolutions and lower willingness of participants. Originality/value This paper proposes a common weight data envelopment analysis (DEA)-based decision model to cope with heterogeneous information collected by the experts to determine the best performing neuromarketing technology. The decision procedure enables the decision-makers to handle the problems of loss of information and multi-granularity by using the fusion of 2-tuple linguistic representation model and fuzzy information. Moreover, a DEA-based common weight model does not require subjective experts’ opinions to weight the evaluation criteria.

Some novel intuitionistic fuzzy information fusion methods in decision making with interaction among attributes

In order to solve multi-attribute decision making problems involving inter-dependence or interaction among the attributes, in this paper, two new Atanassov intuitionistic fuzzy integral operators, namely the Atanassov intuitionistic fuzzy Zhenyuan averaging operator and the Atanassov intuitionistic fuzzy Zhenyuan geometric operator, are proposed. The devised operators fully consider the importance of interactions among different attributes and are highly suitable to handle problems where the attributes are inter-dependent or interactive. Some of their desirable properties are studied. Finally, a series of simulations are provided to demonstrate the advantages and validity of the proposed aggregation operators.

Classification with automatic detection of unknown classes based on SVM and fuzzy MBF: Application to motor diagnosis

Classification algorithms based on data mining tools show good performances for the automatic diagnosis of systems. However, these performances degrade quickly when the database is not exhaustive. This happens, for example, when a new class appears. This class could correspond to a previous unknown fault or to an unknown combination of simultaneous faults. Described algorithm in this paper proposes a solution to this issue. It combines Support Vector Machine (SVM), fuzzy membership functions (mbf) and fuzzy information fusion. It results in the construction of a matrix of memberships to known classes U_class and a vector of membership to unknown classes U_others. Then, from these values, indicators of distance and ambiguity of the observations can be computed. These indicators allow setting a simple rejection rule with a threshold classifier. The algorithm is validated by using Cross-Validation (CV) on experimental data on an induction motor faults supplied by a voltage-source inverter. The results show the good performances of the proposed algorithms and its suitability for transportation systems like aircrafts.

Multi-view fuzzy information fusion in collaborative filtering recommender systems: Application to the urban resilience domain

Recommender systems play an increasingly important role in on-line web services for the personalization and recommendation of content to individual users. The quantity and quality of user-based information has progressed presenting the opportunity to further tailor recommendations to users based on feature view integration. In this work, we propose a hybrid framework which combines a collaborative filtering recommendation system with fuzzy decision-making approaches (based on the use of aggregation functions) to improve the accuracy of domain-specific recommendations. We extend upon the classical, neighborhood-based collaborative filtering process by conflating preference information with user-profile data in the recommendation process. This is performed using intelligent information fusion techniques whereby Ordered Weighted Averaging (OWA) operators and uninorm aggregation functions are implemented in the fusion of multiple views of pairwise similarity degrees between users. To address the shortcoming of generating sensible recommendations to cold users, we incorporate a novel weighting scheme based on fuzzy set modeling within the uninorm-based aggregation of similarity views. We finally outline the application of the proposed approach through an empirical study based in the Urban Resilience domain, along with an example to movie recommendation.

Extracting semantic event information from distributed sensing devices using fuzzy sets

Event detection is a central task for distributed sensor systems and detecting forthcoming events in a timely manner is the main way of minimizing their possibly damaging effects. The state-of-the-art methods for event description and detection always rely on using crisp raw sensory data, which requires huge data transmission as well as is time-consuming. However, even a centralized processing manner cannot ensure accurate event decision due to the imprecision and uncertainty of raw sensor readings. In many cases, users do not care about the raw sensory data or the data format used for in-network processing, but instead they are concerned with the semantic event information, such as “how serious is it?” and “where will it occur?” In addition, the main technique employed by the existing solution for detecting problems is collaboration with neighbors, which requires massive data exchange between neighbors that is highly intensive in terms of wireless communication. In this paper, we introduce an energy-efficient, reliable semantic event information extraction framework using fuzzy sets. Linguistic event variables instead of raw sensor data are used for event information transmission and fusion, and fuzzy method-based semantic event information filtering and fusion algorithms are proposed. Extensive evaluations based on both real-life and synthetic data sets demonstrated that our framework only incurs a small communication cost and it returns interpretable event information with guaranteed accuracy.

Tracking of manoeuvring targets using fuzzy information fusion filter

ABSTRACTThe paper discusses the tracking of a manoeuvring target using fuzzy information fusion filters (FIFFs), based on bearing only measurements received from multiple sensors. While a number of tracking filters including the Kalman filters have been proposed and demonstrated successfully for tracking, the information filter has been reported to be capable of estimating the state of a target even without an initial estimate. However, the estimate is often seen to diverge after a short period. A FIFF has been shown to track small manoeuvres in target following a constant velocity model but is not effective when the target switches to a coordinated turn model. In this paper, the idea of using the FIFF for tracking manoeuvring target is proposed and demonstrated. The filter uses only the bearing measurements in tracking the target and also for detecting the manoeuvre. Using an adaptive turn rate model, the turning rate of the moving target is estimated from the range rate. Here, the range rate is also computed directly from bearing measurements. The detection of the onset of manoeuvre is on the basis of consistent change in the turning rate of the target. A number of different scenarios involving manoeuvring targets are given to demonstrate the efficacy of this approach.

Interval type-2 fuzzy Hamy mean operators and their application in multiple criteria decision making

Interval type-2 fuzzy set (IT2FS) is a widely used tool for handling vagueness and imprecision in real-world problems. To aggregate the interval type-2 fuzzy information, some useful aggregation operators have been developed such as IT2-OWA, IT2-FWA, and IT2-Frank operators. In particular, some aggregation operators which can capture and reflect and interrelationships among aggregated arguments are intensively studied, among which Hamy mean (HM) is an important alternative. However, the traditional HM does not focus on fusion of fuzzy information and the weighted vector of the aggregated arguments. In this study, we extend the HM to accommodate interval type-2 fuzzy environment and show its application to multiple criteria decision making (MADM). In order to simplify the computing, we first define the concept of symmetric triangular interval type-2 fuzzy set and propose some operational laws. Motivated by the idea of HM operator, we develop the symmetric triangular interval type-2 fuzzy Hamy mean (STIT2FHM) operator for aggregating the interval type-2 fuzzy information. Some useful properties such as monotonicity, boundedness, and idempotency are studied in detail. Furthermore, we discuss some special cases with respect to different parameter values of the STIT2FHM operator. For the situations where the input arguments have different importance, we further develop the weighted symmetric triangular interval type-2 fuzzy Hamy mean (WSTIT2FHM) operator to aggregate interval type-2 fuzzy information. Based on it, an approach to MCDM problems with symmetric triangular interval type-2 fuzzy information is developed. Finally, a real-world example concerning tourism recommender system is provided to illustrate the practicality and effectiveness of the proposed method.

On Extending Power-Geometric Operators to Interval-Valued Hesitant Fuzzy Sets and Their Applications to Group Decision Making

Developing aggregation operators for interval-valued hesitant fuzzy sets (IVHFSs) is a technological task we are faced with, because they are specifically important in many problems related to the fusion of interval-valued hesitant fuzzy information. This paper develops several novel kinds of power geometric operators, which are referred to as variable power geometric operators, and extends them to interval-valued hesitant fuzzy environments. A series of generalized interval-valued hesitant fuzzy power geometric (GIVHFG) operators are also proposed to aggregate the IVHFSs to model mandatory requirements. One of the important characteristics of these operators is that objective weights of input arguments are variable with the change of a non-negative parameter. By adjusting the exact value of the parameter, the influence caused by some “false” or “biased” arguments can be reduced. We demonstrate some desirable and useful properties of the proposed aggregation operators and utilize them to develop techniques for multiple criteria group decision making with IVHFSs considering the heterogeneous opinions among individual decision makers. Furthermore, we propose an entropy weights-based fitting approach for objectively obtaining the appropriate value of the parameter. Numerical examples are provided to illustrate the effectiveness of the proposed techniques.

Fuzzy decision fusion of complementary experts based on evolutionary cepstral coefficients for phoneme recognition

Optimal representation of acoustic features is an ongoing challenge in automatic speech recognition research. As an initial step toward this purpose, optimization of filterbanks for the cepstral coefficient using evolutionary optimization methods is proposed in some approaches. However, the large number of optimization parameters required by a filterbank makes it difficult to guarantee that an individual optimized filterbank can provide the best representation for phoneme classification. Moreover, in many cases, a number of potential solutions are obtained. Each solution presents discrimination between specific groups of phonemes. In other words, each filterbank has its own particular advantage. Therefore, the aggregation of the discriminative information provided by filterbanks is demanding challenging task. In this study, the optimization of a number of complementary filterbanks is considered to provide a different representation of speech signals for phoneme classification using the hidden Markov model (HMM). Fuzzy information fusion is used to aggregate the decisions provided by HMMs. Fuzzy theory can effectively handle the uncertainties of classifiers trained with different representations of speech data. In this study, the output of the HMM classifiers of each expert is fused using a fuzzy decision fusion scheme. The decision fusion employed a global and local confidence measurement to formulate the reliability of each classifier based on both the global and local context when making overall decisions. Experiments were conducted based on clean and noisy phonetic samples. The proposed method outperformed conventional Mel frequency cepstral coefficients under both conditions in terms of overall phoneme classification accuracy. The fuzzy fusion scheme was shown to be capable of the aggregation of complementary information provided by each filterbank.

Generalized Bonferroni Mean Operator for Fuzzy Number Intuitionistic Fuzzy Sets and its Application to Multiattribute Decision Making

The Bonferroni mean BM was originally introduced by Bonferroni in 1950. A prominent characteristic of BM is its capability to capture the interrelationship between input arguments. This makes BM useful in various application fields, such as decision making, information retrieval, pattern recognition, and data mining. In this paper, we examine the issue of fuzzy number intuitionistic fuzzy information fusion. We first propose a new generalized Bonferroni mean operator called generalized fuzzy number intuitionistic fuzzy weighted Bonferroni mean GFNIFWBM operator for aggregating fuzzy number intuitionistic fuzzy information. The properties of the new aggregation operator are studied and their special cases are examined. Furthermore, based on the GFNIFWBM operator, an approach to deal with multiattribute decision-making problems under fuzzy number intuitionistic fuzzy environment is developed. Finally, a practical example is provided to illustrate the multiattribute decision-making process.